Today’s smart cities are just the start. We are still early in the exponential digital explosion.

Worldwide, the amount of data is growing by 40 per cent per year; it is expected to reach 44 zettabytes (or 44 trillion gigabytes) by 2025. Technology-driven needs will shape and change the face of smart cities dramatically. Based on our research on smart cities in Europe, Asia and the US we tried to summarise it below.

The ubiquity of data and computing

The Internet of things (IoT) will be further developed and will reduce the gap between the physical world and the digital world. Projections by experts from companies such as IBM and Cisco forecast over 50 billion IoT devices by 2020.

Several factors are driving the IoT boom. Sensor technology is certainly a core enabler of IoT products. Sensors continue to advance toward more robust performance (accuracy, linearity, reliability, no maintenance needed), more miniaturisation (smaller-scale integration, even on a nanotechnology level), more intelligence (self-identification, self-diagnosis, self-calibrating, multi-sensing), more integration (smart multi-sensor systems, complex systems for transforming and processing signals), and more standardised interfaces (economies of scale, importance of codes, compatibility).

In other words, sensor systems are becoming smarter, cheaper and more integrated and are generating huge amounts of data. This trend was already observed in the 1990s but it has totally new dimensions and dynamics today. In addition to the increased performance of sensor technology, enormous technology developments are also occurring in connectivity and data analytics.

Ubiquitous computing is not limited to the IoT world. Our personal behaviour has been changed by the modern smartphone, by which we seem to track everything – with huge impact on multiple areas such as location-based services, mobility, homes and health.

Our personal behaviour has been changed by the modern smartphone, by which we seem to track everything.

In future smart cities, much of this computing power will be driven to the edge of the network – meaning that the devices and sensors will often act in a serverless way, making decisions locally on their own.

But at the same time, bandwidth is projected to grow further, and data transmission performance is increasing exponentially too. For example, the Munich Science Network backbone had a transmission capacity of 10 megabits per second in 1990. This has increased to 100 gigabits per second, or an increase by a factor of 10,000 in only 25 years. We expect that this exponential pace will continue for the next two decades.

Access to bandwidth and speed is already becoming a competitive factor. An early – and, to some people, bizarre – example is Spread Networks’ $300 million fibre-optic connection between Chicago and New York City. This connection goes in a straight line for 1,331 kilometres to cut 3 milliseconds of travel time for the data.

This seemingly tiny time savings provides a competitive advantage for high-frequency traders on the stock exchange. As another example, Microsoft and Facebook have teamed up to lay a 160 terabits-per-second cable across the Atlantic. In the near future, the question of bandwidth within and across cities will become a crucial competitive factor for more and more players. Cities and regions need to and will adapt, but what will happen when the network performance is expected to increase again in the following year? What changes will 5G bring to this game?

Decreasing transaction costs

As noted above, Moore’s law still seems valid today, albeit with some slowdowns since 2003, even though the end of its applicability has been declared periodically in the past. The impact is huge. In 10 years, the performance improvement of digital hardware improves by a factor of 100; in 20 years, by a factor of 10,000. How can city planners, with their perspective as they seek to look two decades or more into the future, incorporate a performance leap of 10,000 times? It would seem that city planning must become more agile to enable future opportunities.

As a result of digitalisation, transaction costs are being reduced radically. The shift from bricks to bits continues, as one banking example illustrates: the same bank transaction costs $4.00 to accomplish at a physical branch, $3.75 at a call centre, $0.85 at an ATM, $0.17 online, and only $0.08 on a mobile device (Statista 2018).

As a result of this trend, companies are shifting their transactions toward the digital space. Consumers’ desire for convenience and speed is the driving force behind this trend. The results are not always desirable, but they are already observable in many cities: local retail shops are closing, and product advice is being reduced or automated. As a consequence, the heart of cities will change.

However, transaction costs also include the efforts involved in searching for a service, determining who the best provider is, understanding their terms and conditions, negotiating with the service provider, and after-sale corrections. Many digital offerings are trying to decrease these costs by collecting and analysing consumer data on searches and transactions.

And as we make decisions in terms of the perceived cost of a choice versus the next best alternative, a near-zero transaction cost world is a nearly effortless world when it comes to active decisions. We will reach a point where the scarcity of one’s time is less and less relevant (Slayback 2018). In this way, the world will become easier to navigate and more accessible for everybody.

A trend toward ecosystems

The hundreds of industries we know today will change as a result of the technological trends of lower transaction costs, ubiquity of mobile interfaces, exponential growth of data, and improved data analytics with artificial intelligence methods. In addition to these technologically driven trends, consumers are emphasising more convenience at lower cost.

As a result, the great number of businesses we know today is expected to further converge toward a few ecosystems. Some people estimate that there will be only 12–15 ecosystems offering products and services by the year 2025.

Within a given ecosystem, it is expected that one-third of total value will be redistributed across today’s industry borders. The key question is: Who wins within such ecosystems? Will the winners be platform players like Amazon, which may eliminate local retail in the cities in the long run, or Airbnb, as it attracts more and more complementary players and creates space pressures for affordable housing for cities?

Some people estimate that there will be only 12–15 ecosystems offering products and services by the year 2025.

Research has shown that competition will be won by creating emotional stickiness for customers and citizens. In other words, those competitors will win who offer people the most complete and most meaningful customer journeys across industry boundaries. For smart cities, this could mean the intelligent linking of virtual data with physical space. Here, the question becomes how much city data can be incorporated into a meaningful customer journey. And how much can a city influence the players to customise their service in a city – or will cities begin to look more and more identical to each other?

The rise of artificial intelligence

Artificial intelligence (AI), which is also evolving very rapidly, can reduce the costs of prediction.

The more these costs can be reduced, the more effectively models can be applied – for example, to level supply and demand for energy providers, or to synchronise traffic lights as Los Angeles has already done. AI can improve traffic flows, reduce CO2 reduction, increase comfort, and – at the same time – control lives in a city.

In the future, machine and deep learning technologies could forecast demand and supply in real time and optimise load dispatch, thereby saving energy and money. For a network that experiences demand ranges between 10 and 18 gigawatts, savings could reach 100 megawatts over periods of one to four hours per day (Buitrago and Asfour 2017).

Multiple Google data centres are now cooled autonomously by AI, reducing energy use by 40 per cent. Two per cent of global greenhouse gas emissions is produced by data centres. This technology could be scaled to reduce energy consumption by other large pieces of infrastructure and improve efficiency without any investment in physical infrastructure.

Alibaba’s City Brain project, originally implemented in the company’s hometown of Hangzhou, increased traffic speed in the district by 15 per cent during its first year of operation. The programme is currently being implemented in Kuala Lumpur, Malaysia and will likely spread elsewhere in the coming years.

Alibaba’s City Brain project, originally implemented in the company’s hometown of Hangzhou, increased traffic speed in the district by 15 per cent during its first year of operation.

So we already see the first highly promising examples of smart-city AI applications, but at the same time we also see some potentially very dangerous implications. Face recognition was in its early stages in the 1990s and generally did not work well for smart home applications, such as access control in elevators or private homes.

Today, the technology is already good enough to perform not only face verification (“My name is Mike Douglas. Can you verify?”) but also the much more difficult task of face identification (“Who am I?”).

This allows for many more applications: smart advertising, where the screen targets the ad toward the identified person’s age, gender, or even mood; finding missing people in cities; helping people with visual impairments to perceive the facial expression of their conversation partner; protecting schools; paying by face – but also an automated tracking of people’s lives as they travel throughout the whole city. For example, every jaywalk could be detected and the violator could be fined immediately.

These developments offer huge benefits, but they also have some critical side effects from an open-society perspective. In Chinese megacities, AI technology is already widely used for surveillance of public spaces. Among the leading AI companies are the Chinese firms Face++ / Megvii and SenseTime, which have some of the highest AI technology standards anywhere in the world and can explore AI’s potential with access to huge datasets and minimal data protection. As of 2018, China had installed over 200 million public surveillance cameras and was responsible for 46 per cent of global surveillance equipment revenues. It is thus not surprising that companies such as SenseTime claim to have huge training datasets, containing two billion images, whereas many publicly available databases have only about 10 million.

China has the ambitious goal of identifying and tracking 1.4 billion people by the year 2020. In combination with the social credit score that the country has also introduced, this is a dangerous development. Citizens are monitored and evaluated, and every crossing at a red traffic light results in a five-point subtraction from one’s personal credit score, whereas a heroic act can improve one’s score. This is today’s version of George Orwell’s “Big Brother.”

In management science, this phenomenon is called algorithmic governance, in that algorithms built by private actors are effectively influencing and shaping citizen behaviour and governing a society. With the use of algorithms come the risks of manipulation, manifestation of biases, censorship, violations of privacy, and more.

Europe has a more traditional sensibility regarding privacy, as was illustrated by the response to the introduction of Google Street View. German-speaking regions have been the most sceptical, whereas in the United States there has been no major resistance. In 2018, Europe introduced its General Data Protection Regulation (GDPR) to give citizens greater control over their personal data. Since the Facebook scandal involving Cambridge Analytica, in which data on many millions of users were misused for commercial purposes, the public has been even more aware of the value and sensitivity of personal data. Yet the GDPR remains quite broad and its implementation is not fully clear for all aspects of and actors in smart cities.

Nevertheless, the overall direction toward recognising privacy as a valuable good in our society is clear.

Unprecedented forms of mobility

The face of city transportation will change enormously in the next decades. New technologies aspire to make individual transportation autonomous (autonomous cars and buses), airborne (flying taxis), and super-fast (hyperloops).

Although many open questions regarding autonomous vehicles remain and sceptics point out that the technology is not ready yet, the eventual advent of such vehicles is inevitable. The advantages are just too overwhelming.

But autonomous cars will change the face of cities (again). All those parking spots will no longer be needed. Traffic will be flowing and not parking – just stopping occasionally to pick up or drop off passengers.

As a system including autonomous vehicles displays its far greater efficiency and fewer cars crowd the roads, how will those relatively vacant multilane highways be used? And when autonomous vehicles are much smarter in finding the best routes and can even communicate with other vehicles, will we need two-way streets at all, or will cities become full of only one-way streets?

Public space will be regained for pedestrians and better quality of life, while the death toll from accidents will decrease by more than 95 per cent. Certainly, however, regulations are a bottleneck. How should we handle the single cases of accidents induced by technology failure? How should we regulate ethical conflicts of the algorithms in cases of unavoidable accidents (known as the trolley problem)? Companies like Mobileye, acquired by Intel in 2017 for $15 billion USD, are pushing the technological limits. Countries like Singapore or Israel are offering to be pilot sites for the new automotive industry.